Fast python callback/event system modeled after Qt Signals

These details have not been verified by PyPI

Project links

Development Status
- 4 - Beta
License
- OSI Approved :: BSD License
Natural Language
- English
Programming Language
Typing
- Typed

Project description

psygnal

Psygnal (pronounced "signal") is a pure python implementation of the observer pattern, with the API of Qt-style Signals with (optional) signature and type checking, and support for threading. It has no dependencies.

This library does not require or use Qt in any way, It simply implements a similar observer pattern API.

Documentation

https://psygnal.readthedocs.io/

Install

pip install psygnal

conda install -c conda-forge psygnal

Usage

The observer pattern is a software design pattern in which an object maintains a list of its dependents ("observers"), and notifies them of any state changes – usually by calling a callback function provided by the observer.

Here is a simple example of using psygnal:

from psygnal import Signal

class MyObject:
    # define one or more signals as class attributes
    value_changed = Signal(str)

# create an instance
my_obj = MyObject()

# You (or others) can connect callbacks to your signals
@my_obj.value_changed.connect
def on_change(new_value: str):
    print(f"The value changed to {new_value}!")

# The object may now emit signals when appropriate,
# (for example in a setter method)
my_obj.value_changed.emit('hi')  # prints "The value changed to hi!"

Much more detail available in the documentation!

Evented Dataclasses

A particularly nice usage of the signal pattern is to emit signals whenever a field of a dataclass changes. Psygnal provides an @evented decorator that will emit a signal whenever a field changes. It is compatible with dataclasses from the standard library, as well as attrs, and pydantic:

from psygnal import evented
from dataclasses import dataclass

@evented
@dataclass
class Person:
    name: str
    age: int = 0

person = Person('John', age=30)

# connect callbacks
@person.events.age.connect
def _on_age_change(new_age: str):
    print(f"Age changed to {new_age}")

person.age = 31  # prints: Age changed to 31

See the dataclass documentation for more details.

Evented Containers

psygnal.containers provides evented versions of mutable data structures (dict, list, set), for cases when you need to monitor mutation:

from psygnal.containers import EventedList

my_list = EventedList([1, 2, 3, 4, 5])

my_list.events.inserted.connect(lambda i, val: print(f"Inserted {val} at index {i}"))
my_list.events.removed.connect(lambda i, val: print(f"Removed {val} at index {i}"))

my_list.append(6)  # Output: Inserted 6 at index 5
my_list.pop()  # Output: Removed 6 at index 5

See the evented containers documentation for more details.

Benchmark history

https://pyapp-kit.github.io/psygnal/

and

https://codspeed.io/pyapp-kit/psygnal

Developers

Compiling

While psygnal is a pure python package, it is compiled with mypyc to increase performance. To test the compiled version locally, you can run:

make build

(which is just an alias for HATCH_BUILD_HOOKS_ENABLE=1 pip install -e .)

Debugging

To disable all compiled files and run the pure python version, you may run:

python -c "import psygnal.utils; psygnal.utils.decompile()"

To return the compiled version, run:

python -c "import psygnal.utils; psygnal.utils.recompile()"

The psygnal._compiled variable will tell you if you're using the compiled version or not.

Project details

These details have not been verified by PyPI

Project links

Development Status
- 4 - Beta
License
- OSI Approved :: BSD License
Natural Language
- English
Programming Language
Typing
- Typed

Release history Release notifications | RSS feed

0.11.1

May 7, 2024

0.11.0

Mar 29, 2024

This version

0.10.2

Mar 12, 2024

0.10.1

Mar 11, 2024

0.10.0

Mar 5, 2024

0.10.0rc4 pre-release

Mar 5, 2024

0.10.0rc3 pre-release

Feb 24, 2024

0.10.0rc2 pre-release

Feb 18, 2024

0.10.0rc1 pre-release

Feb 17, 2024

0.10.0rc0 pre-release

Feb 16, 2024

0.9.5

Nov 13, 2023

0.9.4

Sep 19, 2023

0.9.3

Aug 14, 2023

0.9.2

Aug 12, 2023

0.9.1

May 29, 2023

0.9.0

Apr 7, 2023

0.8.1

Feb 23, 2023

0.8.0

Feb 23, 2023

0.8.0rc1 pre-release

Feb 22, 2023

0.8.0rc0 pre-release

Feb 21, 2023

0.7.2

Feb 11, 2023

0.7.1

Feb 11, 2023

0.7.0

Dec 20, 2022

0.6.1

Nov 13, 2022

0.6.0.post0

Nov 9, 2022

0.6.0

Oct 29, 2022

0.5.0

Oct 15, 2022

0.4.2

Sep 25, 2022

0.4.1

Sep 23, 2022

0.3.5

May 26, 2022

0.3.4

May 2, 2022

0.3.3

Feb 14, 2022

0.3.2

Feb 14, 2022

0.3.1

Feb 12, 2022

0.3.0

Feb 10, 2022

0.2.0

Nov 7, 2021

0.1.4

Oct 17, 2021

0.1.3

Oct 1, 2021

0.1.2

Jul 12, 2021

0.1.1

Jul 7, 2021

0.1.0

Jul 6, 2021

0.1.0rc3 pre-release

Jul 6, 2021

0.1.0rc2 pre-release

Jul 5, 2021

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

psygnal-0.10.2.tar.gz (96.1 kB view details)

Uploaded Mar 12, 2024 Source

Built Distributions

psygnal-0.10.2-py3-none-any.whl (73.1 kB view details)

Uploaded Mar 12, 2024 Python 3

psygnal-0.10.2-cp312-cp312-musllinux_1_1_x86_64.whl (691.1 kB view details)

Uploaded Mar 12, 2024 CPython 3.12 musllinux: musl 1.1+ x86-64

psygnal-0.10.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (708.9 kB view details)

Uploaded Mar 12, 2024 CPython 3.12 manylinux: glibc 2.17+ x86-64

psygnal-0.10.2-cp312-cp312-macosx_10_16_x86_64.whl (424.2 kB view details)

Uploaded Mar 12, 2024 CPython 3.12 macOS 10.16+ x86-64

psygnal-0.10.2-cp312-cp312-macosx_10_16_arm64.whl (404.2 kB view details)

Uploaded Mar 12, 2024 CPython 3.12 macOS 10.16+ ARM64

psygnal-0.10.2-cp311-cp311-musllinux_1_1_x86_64.whl (662.5 kB view details)

Uploaded Mar 12, 2024 CPython 3.11 musllinux: musl 1.1+ x86-64

psygnal-0.10.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (684.4 kB view details)

Uploaded Mar 12, 2024 CPython 3.11 manylinux: glibc 2.17+ x86-64

psygnal-0.10.2-cp311-cp311-macosx_10_16_x86_64.whl (433.0 kB view details)

Uploaded Mar 12, 2024 CPython 3.11 macOS 10.16+ x86-64

psygnal-0.10.2-cp311-cp311-macosx_10_16_arm64.whl (406.0 kB view details)

Uploaded Mar 12, 2024 CPython 3.11 macOS 10.16+ ARM64

psygnal-0.10.2-cp310-cp310-musllinux_1_1_x86_64.whl (672.5 kB view details)

Uploaded Mar 12, 2024 CPython 3.10 musllinux: musl 1.1+ x86-64

psygnal-0.10.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (693.8 kB view details)

Uploaded Mar 12, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

psygnal-0.10.2-cp310-cp310-macosx_10_16_x86_64.whl (439.8 kB view details)

Uploaded Mar 12, 2024 CPython 3.10 macOS 10.16+ x86-64

psygnal-0.10.2-cp310-cp310-macosx_10_16_arm64.whl (412.1 kB view details)

Uploaded Mar 12, 2024 CPython 3.10 macOS 10.16+ ARM64

psygnal-0.10.2-cp39-cp39-musllinux_1_1_x86_64.whl (666.1 kB view details)

Uploaded Mar 12, 2024 CPython 3.9 musllinux: musl 1.1+ x86-64

psygnal-0.10.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (689.5 kB view details)

Uploaded Mar 12, 2024 CPython 3.9 manylinux: glibc 2.17+ x86-64

psygnal-0.10.2-cp39-cp39-macosx_10_16_x86_64.whl (440.2 kB view details)

Uploaded Mar 12, 2024 CPython 3.9 macOS 10.16+ x86-64

psygnal-0.10.2-cp39-cp39-macosx_10_16_arm64.whl (412.0 kB view details)

Uploaded Mar 12, 2024 CPython 3.9 macOS 10.16+ ARM64

psygnal-0.10.2-cp38-cp38-musllinux_1_1_x86_64.whl (663.2 kB view details)

Uploaded Mar 12, 2024 CPython 3.8 musllinux: musl 1.1+ x86-64

psygnal-0.10.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (667.0 kB view details)

Uploaded Mar 12, 2024 CPython 3.8 manylinux: glibc 2.17+ x86-64

psygnal-0.10.2-cp38-cp38-macosx_10_16_x86_64.whl (433.4 kB view details)

Uploaded Mar 12, 2024 CPython 3.8 macOS 10.16+ x86-64

psygnal-0.10.2-cp38-cp38-macosx_10_16_arm64.whl (407.3 kB view details)

Uploaded Mar 12, 2024 CPython 3.8 macOS 10.16+ ARM64

File details

Details for the file psygnal-0.10.2.tar.gz.

File metadata

Download URL: psygnal-0.10.2.tar.gz
Upload date: Mar 12, 2024
Size: 96.1 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for psygnal-0.10.2.tar.gz
Algorithm	Hash digest
SHA256	`adb2c38d5cecfdb1212970a3123079f6d97241ac0e772c3db79418a5cabef079`
MD5	`3066eb7f5b132b5c3448e3d36dbfe6d6`
BLAKE2b-256	`8d7fe06de677dadd73e76bcbbf672c55ae98579fb30be3c3b26e0e946b16a06f`

Algorithm	Hash digest
SHA256	`4c42f76058fa89b1cc128be55d174ff79f06cd8b6cc4e0ed3eadd5d6cf9520b7`
MD5	`f4fd80e1a1696845a02b64feb403a42f`
BLAKE2b-256	`f66b02bb70e420fd368e4b010f28ffdeeffa94ed33262791fee0d48747389fe0`

Algorithm	Hash digest
SHA256	`12a73bebc739a6331a618ecaa7ad0aee410b5009ec7fa0cd5f55e521ee776132`
MD5	`f1578c62ae8a4ec0bb62c50b08509c07`
BLAKE2b-256	`51ba0a3fc341938379d1b5a6ae187992b6026f2bef748d3ef42972321c60dab1`

Algorithm	Hash digest
SHA256	`c56ecf7a5f4125bc0b3721c46efe80756df4ebf394aad435ae8114c95a5316c6`
MD5	`15ce6a243c79970abfdbea1339caf7a1`
BLAKE2b-256	`f6392cdd2c1904b122b227e28c574297a233e05f073cee9339719013e6c8661a`

Algorithm	Hash digest
SHA256	`92e61acfd42ed89b5c6df84ce19eb4d12f9376d2ac8d47f9cd903ae95ebd0813`
MD5	`00e5773cc1bbacb888596c3663535735`
BLAKE2b-256	`65ea3d4bec9989faf1c763c7c15b1d4d180ed36978346fb595af26f85f1a5cbd`

Algorithm	Hash digest
SHA256	`407729dda66b67b0f19c5a49b33c14b657e994ce4807cbdf82efc723a909b0ac`
MD5	`c8be8340b40e26c80fb087dca437e875`
BLAKE2b-256	`9649bf8ca1bb1514e90cf8a958d80269e9410433e94dcbc366b23c6ba40ca7fb`

Algorithm	Hash digest
SHA256	`cd758ca3ef9a4c36589492117cf8a7dbc0f92cef8d3c179f8efb66190ce66f18`
MD5	`b637325e5e11380955dafb8d29cab073`
BLAKE2b-256	`cc1fae845d320c1f59b5f99465ac8b8ee8d3a515a4c2266819fe7b9b0ed9d120`

Algorithm	Hash digest
SHA256	`fffea50e36e1ff3073bc6d8d8481d4d7707e298f9f379e1824de6dee71908045`
MD5	`63ede3674676e1784435e355b89808d0`
BLAKE2b-256	`4f625ffa087855df8e41658a1cb179fb3afa71efed862e0a7dfab3e602545014`

Algorithm	Hash digest
SHA256	`2a453304c48f61613d82461cc6cfa390b972cf41db8f834d3a48ee90471cd818`
MD5	`47ba22f203adc1f6b6e7c920390c21b4`
BLAKE2b-256	`c95972b8a3742186a4fce56114a0ae1df6d75046fecfa007ab17e1ff54e9b0d0`

Algorithm	Hash digest
SHA256	`7bd295bcabcce237bf5d2c05e51c946f83d399bedc92bd0a1d5ab8eb82b7b69c`
MD5	`30d01ca74e40d54476d018f1bd7486cf`
BLAKE2b-256	`9f190609d607b994ee9517906199866fb4c77c3b301663e9253fdfd1af96de34`

Algorithm	Hash digest
SHA256	`09bc8121adf93cca7d5110900e26252ac3b3fe0dacdb96297e1b7d93b80c4525`
MD5	`4a235be4c5bc9e0c5e94c2333daa9a32`
BLAKE2b-256	`52a049d924aba2fa0640dc1c04d381f730f32bb019456da15127c1309109df38`

Algorithm	Hash digest
SHA256	`55378d0eb4821875649da13d9524aad75542479e1f5817f44754622571ac4a75`
MD5	`f3b462d2cb8369e6bfbc3cc378a85172`
BLAKE2b-256	`333cb308a711202a1bc2f538a9900193863bf14eb6474b4232fa1cd35940fa4c`

Algorithm	Hash digest
SHA256	`a3fbbac9445871b57726d41e4d0262f738c6ecc60e3a48572f3fe5829471f1ee`
MD5	`735cbdea9a8365e925c7a90603474d79`
BLAKE2b-256	`00b501e813e8155a4d88a3330496618cfb3f4cb545d0f50941738de1705e9ff3`

Algorithm	Hash digest
SHA256	`004def7d5334dfad12b3d0dcfb7e16c6e3a948c42998eda0472fcc73bb5ae85d`
MD5	`268cbb61614835b46d663a1bf08b14dc`
BLAKE2b-256	`c38c7106531209d9f93bf1189a7f08a76739e08ac7011384075ebf4c8a3b706c`

Algorithm	Hash digest
SHA256	`a4738f8b53cd3ad268e474e4570f734ac4a23951123d14a1573e199c5294b103`
MD5	`171ac9699fdf80bcc18840e9448de980`
BLAKE2b-256	`7eeb34d066a2a1e30122dabe9364c45f20b2bb9e771117d111d91ef8b9c1232e`

Algorithm	Hash digest
SHA256	`6e56ef3992e3f09e76cacec79563ec4a65c584f86742fc099adbf7d2f61e94ef`
MD5	`3622e2e7ce56270baef73982a3fc1534`
BLAKE2b-256	`1873c04e99517965640992a2e4180fc949de9555a0d68c12254276ddb4879c40`

Algorithm	Hash digest
SHA256	`e9a5ed1a1843e81928490919a09af33301fb9d7920195bbae13f050e4664e4ae`
MD5	`b8ff8ef296337a8be5992eca161c4743`
BLAKE2b-256	`350b66ac68145164b509191bdd00fdb79fcba405e5eca5c7affd99174a9a7d06`

Algorithm	Hash digest
SHA256	`9b904424fb994e6c0088b3aab1d15305d8f27fa716c435dd5ebd5d58f1d6087b`
MD5	`fd3981f67a3831b084812632d3423a27`
BLAKE2b-256	`5f32373cd282a6a0cc23da2dea815c4e86403963ace9e70ff2afa40ab6d66dd3`

Algorithm	Hash digest
SHA256	`b9cf48ef4be9ef473edb68fb89241a0d6ee787e1c4c8c81a19a41b16db90bcb3`
MD5	`4510c932728cf8144fb0e53070b5c19a`
BLAKE2b-256	`11957bb56bd6ac370ffd4d12ab0c3fec3d4e8571f28c7968be5d2c78f5acdbfa`

Algorithm	Hash digest
SHA256	`09c6cdd85976c1d8b8064481ec581d72efaa52eeff232e479454c412260d26e9`
MD5	`3306e8a8a1d6e50f4cf1ae11e4f87ce0`
BLAKE2b-256	`242fef5fcf3a32a5f027eb007f60b1b70b9d20f314a02318bec3e34ee1564b0a`

Algorithm	Hash digest
SHA256	`dc6a974700a1273fffe0ceecb0f4e331d41c570a86047f89383811c94d7b9157`
MD5	`a2327115b5f037e187ffa3bd1774fa7a`
BLAKE2b-256	`718548d496dde1bcca8083cc800e05e2729e3d623023bef292984c5e8319e6b1`

psygnal 0.10.2

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

psygnal

Documentation

Install

Usage

Evented Dataclasses

Evented Containers

Benchmark history

Developers

Compiling

Debugging

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

Algorithm	Hash digest
SHA256	`3ace1fa396190e3a08c8a8f3e9d59c030535987d59950da19589c6003a0b02f6`
MD5	`0cfd9f7f3b37c7ce46a92c8b7fd48f00`
BLAKE2b-256	`6dd3b77b8422341fb8d8216db19f781fea5b7ee859fc8ea3a52e7bd2b989f3e6`